A typical black and white image contains both spacial and density information. The image may be digitized for display on a computer screen by sampling every pixel in the image to achieve a digital value of the density in that pixel. Alternatively, the image may be generated by, for example, exposing an object to an X-ray beam transmitted from an X-ray source and measuring the resulting transmitted beam using a detector array disposed behind the object. A method of arranging the X-ray source and detector is to form a narrow pencil beam of X-rays at the output of the X-ray source. This pencil beam is then moved vertically along a line, from top to bottom, exposing a single narrow detector to the X-rays transmitted through the object. The location of the pencil beam then determines the position of the image pixel along the vertical line.
Typically, the object to be imaged is moved through the X-ray beam and detector so that all of the spacial locations of the object are exposed to the beam. At each location, the output electrical signal of the detector is sampled using a sampling circuit and a signal is generated for display on the computer screen. For this single-detector system, the spacial resolution is limited by the number of samples taken during each vertical pass of the pencil beam and the displacement of the object between samples.
At each spacial location, a number is typically assigned to the amount of energy observed by the detector at that location. The resulting number of this process, called quantization, is proportional to the electrical signal at the output of the detector. The quantization process generally involves analog-to-digital conversion, where the input energy to the detector is represented by a finite number of density values. However, if the energy at a particular pixel location does not equal one of the finite density values, it is assigned the value of a closest finite value. For example, if the finite density values range from 1.0 to 10.0 and the density value at a pixel was observed by the detector to be 4.3, the quantizer would assign that pixel the density value 4.0 (the closest one). The resulting error in assignment of such a value is known as a quantization error.
FIGS. 1A-C shows a prior art technique for conventional digital image formation. FIG. 1A depicts an object 100 to be imaged. The spacial sampling process may be considered as overlaying a grid 125 on the object 100, as shown in FIG. 1B, with the detector (not shown) examining the energy output from each grid box 126 and converting it to an electrical signal. The quantization process then assigns a density number to the electrical signal. The result, which is shown in FIG. 1C as a matrix 130 of numbers, is the digital representation of the image. Each spacial location 136 in the image (or grid) to which a number is assigned is a pixel and the size of the sampling grid is typically determined by the number of pixels on each side of the grid, e.g., 1000.times.1000. After the digital image is obtained, a digital-to-analog converter (not shown) may be used to convert the matrix of pixels back to an image that can be viewed on the computer display.
The quality of representation of a digital image is typically determined by the number of pixels and by the number of levels used in the quantization, i.e., how coarse or fine is the quantization, while the number of pixels needed to form the digital image increases as the spacial resolution improves. To achieve a quality digital image, it is thus apparent that a large amount of information is typically needed to represent the image. There are several reasons why the acquisition of an image with many pixel samples may be difficult (or even impossible). For example, the X-ray source used in the illustrative embodiment of the invention herein is a high energy pulsed linear accelerator that is limited in its pulse repetition rate. To acquire the typical image described above, the X-ray source must be pulsed approximately one million times which, practically, would consume an unreasonably long period of time.
In addition, transmitting such image information over a communication channel, such as a telephone line, from computer to computer may be expensive. For example, a 1000.times.1000 image having 16 bits/pixel (including grey level representation) requires 2 megabytes (MB) of information to describe the image. At data rates of at least 2400 bits/second, it would require about 1.8 hours to transmit this information, which is time consuming and costly.
One way to minimize the amount of information needed to represent an image is to reduce the number of pixels used to display the image. Thus, instead of recording data for 1,000,000 pixels, i.e., the 1000.times.1000 image, a system may be configured to record data for only 100,000 pixels. This technique reduces the number of pixels by effectively making each pixel 10 times larger in area; however, it also produces a blurred image.
Therefore, it is an object of the present invention to minimize the number of sampled pixels needed to represent an object as a non-blurred image.